A new family of gradient-based adaptive filtering algorithms with variable step size

Under a uniform framework, this paper develops a new family of adaptive filtering algorithms, where the wellknown normalized LMS algorithm and normalized constant modulus algorithm (CMA) are included. They all update the weight according to the gradient descent method, but this time a variable and relatively optimal step size is used instead of a constant one. Some application examples are also given to show their efficiencies.